Statistical Learning Within Objects
通过三个实验，研究了年轻成年人能否在物体内部进行统计学习，发现注意力会优先分配到物体相关部位，且这种偏好能泛化到新视角。
Research has recently shown that efficient selection relies on the implicit extraction of environmental regularities, known as statistical learning . Although this has been demonstrated for scenes, similar learning arguably also occurs for objects. To test this, we developed a paradigm that allowed us to track attentional priority at specific object locations irrespective of the object’s orientation in three experiments with young adults (all N s = 80). Experiments 1a and 1b established within-object statistical learning by demonstrating increased attentional priority at relevant object parts (e.g., hammerhead). Experiment 2 extended this finding by demonstrating that learned priority generalized to viewpoints in which learning never took place. Together, these findings demonstrate that as a function of statistical learning, the visual system not only is able to tune attention relative to specific locations in space but also can develop preferential biases for specific parts of an object independently of the viewpoint of that object.